What’s new in this chapter

There are no major changes in this chapter. There is a new, brief discussion of the typing.Protocol in a tip box near the end of “Protocols and Duck Typing”.

In “A Slice-Aware __getitem__”, the implementation of __getitem__ in Example 12-6 is shorter and more robust than the example in the first edition, thanks to duck typing and operator.index. This change carried over to later implementations of Vector in this chapter and in [Link to Come].

Let’s get started.

Vector: A User-Defined Sequence Type

Our strategy to implement Vector will be to use composition, not inheritance. We’ll store the components in an array of floats, and will implement the methods needed for our Vector to behave like an immutable flat sequence.

But before we implement the sequence methods, let’s make sure we have a baseline implementation of Vector that is compatible with our earlier Vector2d class—except where such compatibility would not make sense.

Vector Take #1: Vector2d Compatible

The first version of Vector should be as compatible as possible with our earlier Vector2d class.

However, by design, the Vector constructor is not compatible with the Vector2d constructor. We could make Vector(3, 4) and Vector(3, 4, 5) work, by taking arbitrary arguments with *args in __init__, but the best practice for a sequence constructor is to take the data as an iterable argument in the constructor, like all built-in sequence types do. Example 12-1 shows some ways of instantiating our new Vector objects.

>>> Vector([3.1, 4.2])
Vector([3.1, 4.2])
>>> Vector((3, 4, 5))
Vector([3.0, 4.0, 5.0])
>>> Vector(range(10))
Vector([0.0, 1.0, 2.0, 3.0, 4.0, ...])

Apart from new constructor signature, I made sure every test I did with Vector2d (e.g., Vector2d(3, 4)) passed and produced the same result with a two-component Vector([3, 4]).

Example 12-2 lists the implementation of our first version of Vector (this example builds on the code shown in Examples 11-2 and 11-3).

from array import array
import reprlib
import math


class Vector:
    typecode = 'd'

    def __init__(self, components):
        self._components = array(self.typecode, components)  

    def __iter__(self):
        return iter(self._components)  

    def __repr__(self):
        components = reprlib.repr(self._components)  
        components = components[components.find('['):-1]  
        return 'Vector({})'.format(components)

    def __str__(self):
        return str(tuple(self))

    def __bytes__(self):
        return (bytes([ord(self.typecode)]) +
                bytes(self._components))  

    def __eq__(self, other):
        return tuple(self) == tuple(other)

    def __abs__(self):
        return math.sqrt(sum(x * x for x in self))  

    def __bool__(self):
        return bool(abs(self))

    @classmethod
    def frombytes(cls, octets):
        typecode = chr(octets[0])
        memv = memoryview(octets[1:]).cast(typecode)
        return cls(memv)  

The self._components instance “protected” attribute will hold an array with the Vector components.

To allow iteration, we return an iterator over self._components.¹

Use reprlib.repr() to get a limited-length representation of self._components (e.g., array('d', [0.0, 1.0, 2.0, 3.0, 4.0, ...])).

Remove the array('d', prefix and the trailing ) before plugging the string into a Vector constructor call.

Build a bytes object directly from self._components.

We can’t use hypot anymore, so we sum the squares of the components and compute the sqrt of that.

The only change needed from the earlier frombytes is in the last line: we pass the memoryview directly to the constructor, without unpacking with * as we did before.

The way I used reprlib.repr deserves some elaboration. That function produces safe representations of large or recursive structures by limiting the length of the output string and marking the cut with '...'. I wanted the repr of a Vector to look like Vector([3.0, 4.0, 5.0]) and not Vector(array('d', [3.0, 4.0, 5.0])), because the fact that there is an array inside a Vector is an implementation detail. Because these constructor calls build identical Vector objects, I prefer the simpler syntax using a list argument.

When coding __repr__, I could have produced the simplified components display with this expression: reprlib.repr(list(self._components)). However, this would be wasteful, as I’d be copying every item from self._components to a list just to use the list repr. Instead, I decided to apply reprlib.repr to the self._components array directly, and then chop off the characters outside of the []. That’s what the second line of __repr__ does in Example 12-2.

Note that the __str__, __eq__, and __bool__ methods are unchanged from Vector2d, and only one character was changed in frombytes (a * was removed in the last line). This is one of the benefits of making the original Vector2d iterable.

By the way, we could have subclassed Vector from Vector2d, but I chose not to do it for two reasons. First, the incompatible constructors really make subclassing not advisable. I could work around that with some clever parameter handling in __init__, but the second reason is more important: I want Vector to be a standalone example of a class implementing the sequence protocol. That’s what we’ll do next, after a discussion of the term protocol.

Protocols and Duck Typing

As early as Chapter 1, we saw that you don’t need to inherit from any special class to create a fully functional sequence type in Python; you just need to implement the methods that fulfill the sequence protocol. But what kind of protocol are we talking about?

In the context of object-oriented programming, a protocol is an informal interface, defined only in documentation and not in code. For example, the sequence protocol in Python entails just the __len__ and __getitem__ methods. Any class Spam that implements those methods with the standard signature and semantics can be used anywhere a sequence is expected. Whether Spam is a subclass of this or that is irrelevant; all that matters is that it provides the necessary methods. We saw that in Example 1-1, reproduced here in Example 12-3.

import collections

Card = collections.namedtuple('Card', ['rank', 'suit'])

class FrenchDeck:
    ranks = [str(n) for n in range(2, 11)] + list('JQKA')
    suits = 'spades diamonds clubs hearts'.split()

    def __init__(self):
        self._cards = [Card(rank, suit) for suit in self.suits
                                        for rank in self.ranks]

    def __len__(self):
        return len(self._cards)

    def __getitem__(self, position):
        return self._cards[position]

The FrenchDeck class in Example 12-3 takes advantage of many Python facilities because it implements the sequence protocol, even if that is not declared anywhere in the code. Any experienced Python coder will look at it and understand that it is a sequence, even if it subclasses object. We say it is a sequence because it behaves like one, and that is what matters.

This became known as duck typing, after Alex Martelli’s post quoted at the beginning of this chapter.

Because protocols are informal and unenforced, you can often get away with implementing just part of a protocol, if you know the specific context where a class will be used. For example, to support iteration, only __getitem__ is required; there is no need to provide __len__.

We’ll now implement the sequence protocol in Vector, initially without proper support for slicing, but later adding that.

Vector Take #2: A Sliceable Sequence

As we saw with the FrenchDeck example, supporting the sequence protocol is really easy if you can delegate to a sequence attribute in your object, like our self._components array. These __len__ and __getitem__ one-liners are a good start:

class Vector:
    # many lines omitted
    # ...

    def __len__(self):
        return len(self._components)

    def __getitem__(self, index):
        return self._components[index]

With these additions, all of these operations now work:

>>> v1 = Vector([3, 4, 5])
>>> len(v1)
3
>>> v1[0], v1[-1]
(3.0, 5.0)
>>> v7 = Vector(range(7))
>>> v7[1:4]
array('d', [1.0, 2.0, 3.0])

As you can see, even slicing is supported—but not very well. It would be better if a slice of a Vector was also a Vector instance and not a array. The old FrenchDeck class has a similar problem: when you slice it, you get a list. In the case of Vector, a lot of functionality is lost when slicing produces plain arrays.

Consider the built-in sequence types: every one of them, when sliced, produces a new instance of its own type, and not of some other type.

To make Vector produce slices as Vector instances, we can’t just delegate the slicing to array. We need to analyze the arguments we get in __getitem__ and do the right thing.

Now, let’s see how Python turns the syntax my_seq[1:3] into arguments for my_seq.__getitem__(...).

>>> class MySeq:
...     def __getitem__(self, index):
...         return index  
...
>>> s = MySeq()
>>> s[1]  
1
>>> s[1:4]  
slice(1, 4, None)
>>> s[1:4:2]  
slice(1, 4, 2)
>>> s[1:4:2, 9]  
(slice(1, 4, 2), 9)
>>> s[1:4:2, 7:9]  
(slice(1, 4, 2), slice(7, 9, None))

>>> slice  
<class 'slice'>
>>> dir(slice) 
['__class__', '__delattr__', '__dir__', '__doc__', '__eq__',
 '__format__', '__ge__', '__getattribute__', '__gt__',
 '__hash__', '__init__', '__le__', '__lt__', '__ne__',
 '__new__', '__reduce__', '__reduce_ex__', '__repr__',
 '__setattr__', '__sizeof__', '__str__', '__subclasshook__',
 'indices', 'start', 'step', 'stop']

>>> slice(None, 10, 2).indices(5)  
(0, 5, 2)
>>> slice(-3, None, None).indices(5)  
(2, 5, 1)

    def __len__(self):
        return len(self._components)

    def __getitem__(self, key):
        if isinstance(key, slice):  
            cls = type(self)  
            return cls(self._components[key])  
        index = operator.index(key)  
        return self._components[index]  

    >>> v7 = Vector(range(7))
    >>> v7[-1]  
    6.0
    >>> v7[1:4]  
    Vector([1.0, 2.0, 3.0])
    >>> v7[-1:]  
    Vector([6.0])
    >>> v7[1,2]  
    Traceback (most recent call last):
      ...
    TypeError: 'tuple' object cannot be interpreted as an integer

Vector Take #3: Dynamic Attribute Access

In the evolution from Vector2d to Vector, we lost the ability to access vector components by name (e.g., v.x, v.y). We are now dealing with vectors that may have a large number of components. Still, it may be convenient to access the first few components with shortcut letters such as x, y, z instead of v[0], v[1] and v[2].

Here is the alternative syntax we want to provide for reading the first four components of a vector:

>>> v = Vector(range(10))
>>> v.x
0.0
>>> v.y, v.z, v.t
(1.0, 2.0, 3.0)

In Vector2d, we provided read-only access to x and y using the @property decorator (Example 11-7). We could write four properties in Vector, but it would be tedious. The __getattr__ special method provides a better way.

The __getattr__ method is invoked by the interpreter when attribute lookup fails. In simple terms, given the expression my_obj.x, Python checks if the my_obj instance has an attribute named x; if not, the search goes to the class (my_obj.__class__), and then up the inheritance graph.² If the x attribute is not found, then the __getattr__ method defined in the class of my_obj is called with self and the name of the attribute as a string (e.g., 'x').

Example 12-8 lists our __getattr__ method. Essentially it checks whether the attribute being sought is one of the letters xyzt and if so, returns the corresponding vector component.

    shortcut_names = 'xyzt'

    def __getattr__(self, name):
        cls = type(self)  
        if len(name) == 1:  
            pos = cls.shortcut_names.find(name)  
            if 0 <= pos < len(self._components):  
                return self._components[pos]
        msg = '{.__name__!r} object has no attribute {!r}'  
        raise AttributeError(msg.format(cls, name))

Get the Vector class for later use.

If the name is one character, it may be one of the shortcut_names.

Find position of 1-letter name; str.find would also locate 'yz' and we don’t want that, this is the reason for the test above.

If the position is within range, return the array element.

If either test failed, raise AttributeError with a standard message text.

It’s not hard to implement __getattr__, but in this case it’s not enough. Consider the bizarre interaction in Example 12-9.

>>> v = Vector(range(5))
>>> v
Vector([0.0, 1.0, 2.0, 3.0, 4.0])
>>> v.x  
0.0
>>> v.x = 10  
>>> v.x  
10
>>> v
Vector([0.0, 1.0, 2.0, 3.0, 4.0])  

Access element v[0] as v.x.

Assign new value to v.x. This should raise an exception.

Reading v.x shows the new value, 10.

However, the vector components did not change.

Can you explain what is happening? In particular, why the second time v.x returns 10 if that value is not in the vector components array? If you don’t know right off the bat, study the explanation of __getattr__ given right before Example 12-8. It’s a bit subtle, but a very important foundation to understand a lot of what comes later in the book.

The inconsistency in Example 12-9 was introduced because of the way __getattr__ works: Python only calls that method as a fall back, when the object does not have the named attribute. However, after we assign v.x = 10, the v object now has an x attribute, so __getattr__ will no longer be called to retrieve v.x: the interpreter will just return the value 10 that is bound to v.x. On the other hand, our implementation of __getattr__ pays no attention to instance attributes other than self._components, from where it retrieves the values of the “virtual attributes” listed in shortcut_names.

We need to customize the logic for setting attributes in our Vector class in order to avoid this inconsistency.

Recall that in the latest Vector2d examples from Chapter 11, trying to assign to the .x or .y instance attributes raised AttributeError. In Vector we want the same exception with any attempt at assigning to all single-letter lowercase attribute names, just to avoid confusion. To do that, we’ll implement __setattr__ as listed in Example 12-10.

    def __setattr__(self, name, value):
        cls = type(self)
        if len(name) == 1:  
            if name in cls.shortcut_names:  
                error = 'readonly attribute {attr_name!r}'
            elif name.islower():  
                error = "can't set attributes 'a' to 'z' in {cls_name!r}"
            else:
                error = ''  
            if error:  
                msg = error.format(cls_name=cls.__name__, attr_name=name)
                raise AttributeError(msg)
        super().__setattr__(name, value)  

Special handling for single-character attribute names.

If name is one of xyzt, set specific error message.

If name is lowercase, set error message about all single-letter names.

Otherwise, set blank error message.

If there is a nonblank error message, raise AttributeError.

Default case: call __setattr__ on superclass for standard behavior.

While choosing the error message to display with AttributeError, my first check was the behavior of the built-in complex type, because they are immutable and have a pair of data attributes real and imag. Trying to change either of those in a complex instance raises AttributeError with the message "can't set attribute". On the other hand, trying to set a read-only attribute protected by a property as we did in “A Hashable Vector2d” produces the message "readonly attribute". I drew inspiration from both wordings to set the error string in __setitem__, but was more explicit about the forbidden attributes.

Note that we are not disallowing setting all attributes, only single-letter, lowercase ones, to avoid confusion with the supported read-only attributes x, y, z, and t.

Even without supporting writing to the Vector components, here is an important takeaway from this example: very often when you implement __getattr__ you need to code __setattr__ as well, to avoid inconsistent behavior in your objects.

If we wanted to allow changing components, we could implement __setitem__ to enable v[0] = 1.1 and/or __setattr__ to make v.x = 1.1 work. But Vector will remain immutable because we want to make it hashable in the coming section.

Vector Take #4: Hashing and a Faster ==

Once more we get to implement a __hash__ method. Together with the existing __eq__, this will make Vector instances hashable.

The __hash__ in Example 11-8 simply computed hash(self.x) ^ hash(self.y). We now would like to apply the ^ (xor) operator to the hashes of every component, in succession, like this: v[0] ^ v[1] ^ v[2]…. That is what the functools.reduce function is for. Previously I said that reduce is not as popular as before,³ but computing the hash of all vector components is a perfect job for it. Figure 12-1 depicts the general idea of the reduce function.

Figure 12-1. Reducing functions—reduce, sum, any, all—produce a single aggregate result from a sequence or from any finite iterable object.

So far we’ve seen that functools.reduce() can be replaced by sum(), but now let’s properly explain how it works. The key idea is to reduce a series of values to a single value. The first argument to reduce() is a two-argument function, and the second argument is an iterable. Let’s say we have a two-argument function fn and a list lst. When you call reduce(fn, lst), fn will be applied to the first pair of elements—fn(lst[0], lst[1])—producing a first result, r1. Then fn is applied to r1 and the next element—fn(r1, lst[2])—producing a second result, r2. Now fn(r2, lst[3]) is called to produce r3 … and so on until the last element, when a single result, rN, is returned.

Here is how you could use reduce to compute 5! (the factorial of 5):

>>> 2 * 3 * 4 * 5  # the result we want: 5! == 120
120
>>> import functools
>>> functools.reduce(lambda a,b: a*b, range(1, 6))
120

Back to our hashing problem, Example 12-11 shows the idea of computing the aggregate xor by doing it in three ways: with a for loop and two reduce calls.

>>> n = 0
>>> for i in range(1, 6):  
...     n ^= i
...
>>> n
1
>>> import functools
>>> functools.reduce(lambda a, b: a^b, range(6))  
1
>>> import operator
>>> functools.reduce(operator.xor, range(6))  
1

Aggregate xor with a for loop and an accumulator variable.

functools.reduce using an anonymous function.

functools.reduce replacing custom lambda with operator.xor.

From the alternatives in Example 12-11, the last one is my favorite, and the for loop comes second. What is your preference?

As seen in “The operator Module”, operator provides the functionality of all Python infix operators in function form, lessening the need for lambda.

To code Vector.__hash__ in my preferred style, we need to import the functools and operator modules. Example 12-12 shows the relevant changes.

from array import array
import reprlib
import math
import functools  
import operator  


class Vector:
    typecode = 'd'

    # many lines omitted in book listing...

    def __eq__(self, other):  
        return tuple(self) == tuple(other)

    def __hash__(self):
        hashes = (hash(x) for x in self._components)  
        return functools.reduce(operator.xor, hashes, 0)  

    # more lines omitted...

Import functools to use reduce.

Import operator to use xor.

No change to __eq__; I listed it here because it’s good practice to keep __eq__ and __hash__ close in source code, because they need to work together.

Create a generator expression to lazily compute the hash of each component.

Feed hashes to reduce with the xor function to compute the aggregate hash code; the third argument, 0, is the initializer (see next warning).

As implemented, the __hash__ method in Example 12-12 is a perfect example of a map-reduce computation (Figure 12-2).

Figure 12-2. Map-reduce: apply function to each item to generate a new series (map), then compute aggregate (reduce)

The mapping step produces one hash for each component, and the reduce step aggregates all hashes with the xor operator. Using map instead of a genexp makes the mapping step even more visible:

    def __hash__(self):
        hashes = map(hash, self._components)
        return functools.reduce(operator.xor, hashes)

While we are on the topic of reducing functions, we can replace our quick implementation of __eq__ with another one that will be cheaper in terms of processing and memory, at least for large vectors. As introduced in Example 11-2, we have this very concise implementation of __eq__:

    def __eq__(self, other):
        return tuple(self) == tuple(other)

This works for Vector2d and for Vector—it even considers Vector([1, 2]) equal to (1, 2), which may be a problem, but we’ll overlook that for now.⁴ But for Vector instances that may have thousands of components, it’s very inefficient. It builds two tuples copying the entire contents of the operands just to use the __eq__ of the tuple type. For Vector2d (with only two components), it’s a good shortcut, but not for the large multidimensional vectors. A better way of comparing one Vector to another Vector or iterable would be Example 12-13.

    def __eq__(self, other):
        if len(self) != len(other):  
            return False
        for a, b in zip(self, other):  
            if a != b:  
                return False
        return True  

If the len of the objects are different, they are not equal.

zip produces a generator of tuples made from the items in each iterable argument. See “The Awesome zip” if zip is new to you. The len comparison above is needed because zip stops producing values without warning as soon as one of the inputs is exhausted.

As soon as two components are different, exit returning False.

Otherwise, the objects are equal.

Example 12-13 is efficient, but the all function can produce the same aggregate computation of the for loop in one line: if all comparisons between corresponding components in the operands are True, the result is True. As soon as one comparison is False, all returns False. Example 12-14 shows how __eq__ looks using all.

    def __eq__(self, other):
        return len(self) == len(other) and all(a == b for a, b in zip(self, other))

Note that we first check that the operands have equal length, because zip will stop at the shortest operand.

Example 12-14 is the implementation we choose for __eq__ in vector_v4.py.

We wrap up this chapter by bringing back the __format__ method from Vector2d to Vector.

Vector Take #5: Formatting

The __format__ method of Vector will resemble that of Vector2d, but instead of providing a custom display in polar coordinates, Vector will use spherical coordinates—also known as “hyperspherical” coordinates, because now we support n dimensions, and spheres are “hyperspheres” in 4D and beyond.⁵ Accordingly, we’ll change the custom format suffix from 'p' to 'h'.

For example, given a Vector object in 4D space (len(v) == 4), the 'h' code will produce a display like <r, Φ₁, Φ₂, Φ₃> where r is the magnitude (abs(v)) and the remaining numbers are the angular components Φ₁, Φ₂, Φ₃.

Here are some samples of the spherical coordinate format in 4D, taken from the doctests of vector_v5.py (see Example 12-16):

>>> format(Vector([-1, -1, -1, -1]), 'h')
'<2.0, 2.0943951023931957, 2.186276035465284, 3.9269908169872414>'
>>> format(Vector([2, 2, 2, 2]), '.3eh')
'<4.000e+00, 1.047e+00, 9.553e-01, 7.854e-01>'
>>> format(Vector([0, 1, 0, 0]), '0.5fh')
'<1.00000, 1.57080, 0.00000, 0.00000>'

Before we can implement the minor changes required in __format__, we need to code a pair of support methods: angle(n) to compute one of the angular coordinates (e.g., Φ₁), and angles() to return an iterable of all angular coordinates. I’ll not describe the math here; if you’re curious, Wikipedia’s "n-sphere” entry has the formulas I used to calculate the spherical coordinates from the Cartesian coordinates in the Vector components array.

Example 12-16 is a full listing of vector_v5.py consolidating all we’ve implemented since “Vector Take #1: Vector2d Compatible” and introducing custom formatting.

"""
A multidimensional ``Vector`` class, take 5

A ``Vector`` is built from an iterable of numbers::

    >>> Vector([3.1, 4.2])
    Vector([3.1, 4.2])
    >>> Vector((3, 4, 5))
    Vector([3.0, 4.0, 5.0])
    >>> Vector(range(10))
    Vector([0.0, 1.0, 2.0, 3.0, 4.0, ...])


Tests with two dimensions (same results as ``vector2d_v1.py``)::

    >>> v1 = Vector([3, 4])
    >>> x, y = v1
    >>> x, y
    (3.0, 4.0)
    >>> v1
    Vector([3.0, 4.0])
    >>> v1_clone = eval(repr(v1))
    >>> v1 == v1_clone
    True
    >>> print(v1)
    (3.0, 4.0)
    >>> octets = bytes(v1)
    >>> octets
    b'd\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x10@'
    >>> abs(v1)
    5.0
    >>> bool(v1), bool(Vector([0, 0]))
    (True, False)


Test of ``.frombytes()`` class method:

    >>> v1_clone = Vector.frombytes(bytes(v1))
    >>> v1_clone
    Vector([3.0, 4.0])
    >>> v1 == v1_clone
    True


Tests with three dimensions::

    >>> v1 = Vector([3, 4, 5])
    >>> x, y, z = v1
    >>> x, y, z
    (3.0, 4.0, 5.0)
    >>> v1
    Vector([3.0, 4.0, 5.0])
    >>> v1_clone = eval(repr(v1))
    >>> v1 == v1_clone
    True
    >>> print(v1)
    (3.0, 4.0, 5.0)
    >>> abs(v1)  # doctest:+ELLIPSIS
    7.071067811...
    >>> bool(v1), bool(Vector([0, 0, 0]))
    (True, False)


Tests with many dimensions::

    >>> v7 = Vector(range(7))
    >>> v7
    Vector([0.0, 1.0, 2.0, 3.0, 4.0, ...])
    >>> abs(v7)  # doctest:+ELLIPSIS
    9.53939201...


Test of ``.__bytes__`` and ``.frombytes()`` methods::

    >>> v1 = Vector([3, 4, 5])
    >>> v1_clone = Vector.frombytes(bytes(v1))
    >>> v1_clone
    Vector([3.0, 4.0, 5.0])
    >>> v1 == v1_clone
    True


Tests of sequence behavior::

    >>> v1 = Vector([3, 4, 5])
    >>> len(v1)
    3
    >>> v1[0], v1[len(v1)-1], v1[-1]
    (3.0, 5.0, 5.0)


Test of slicing::

    >>> v7 = Vector(range(7))
    >>> v7[-1]
    6.0
    >>> v7[1:4]
    Vector([1.0, 2.0, 3.0])
    >>> v7[-1:]
    Vector([6.0])
    >>> v7[1,2]
    Traceback (most recent call last):
      ...
    TypeError: 'tuple' object cannot be interpreted as an integer


Tests of dynamic attribute access::

    >>> v7 = Vector(range(10))
    >>> v7.x
    0.0
    >>> v7.y, v7.z, v7.t
    (1.0, 2.0, 3.0)

Dynamic attribute lookup failures::

    >>> v7.k
    Traceback (most recent call last):
      ...
    AttributeError: 'Vector' object has no attribute 'k'
    >>> v3 = Vector(range(3))
    >>> v3.t
    Traceback (most recent call last):
      ...
    AttributeError: 'Vector' object has no attribute 't'
    >>> v3.spam
    Traceback (most recent call last):
      ...
    AttributeError: 'Vector' object has no attribute 'spam'


Tests of hashing::

    >>> v1 = Vector([3, 4])
    >>> v2 = Vector([3.1, 4.2])
    >>> v3 = Vector([3, 4, 5])
    >>> v6 = Vector(range(6))
    >>> hash(v1), hash(v3), hash(v6)
    (7, 2, 1)


Most hash codes of non-integers vary from a 32-bit to 64-bit CPython build::

    >>> import sys
    >>> hash(v2) == (384307168202284039 if sys.maxsize > 2**32 else 357915986)
    True


Tests of ``format()`` with Cartesian coordinates in 2D::

    >>> v1 = Vector([3, 4])
    >>> format(v1)
    '(3.0, 4.0)'
    >>> format(v1, '.2f')
    '(3.00, 4.00)'
    >>> format(v1, '.3e')
    '(3.000e+00, 4.000e+00)'


Tests of ``format()`` with Cartesian coordinates in 3D and 7D::

    >>> v3 = Vector([3, 4, 5])
    >>> format(v3)
    '(3.0, 4.0, 5.0)'
    >>> format(Vector(range(7)))
    '(0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0)'


Tests of ``format()`` with spherical coordinates in 2D, 3D and 4D::

    >>> format(Vector([1, 1]), 'h')  # doctest:+ELLIPSIS
    '<1.414213..., 0.785398...>'
    >>> format(Vector([1, 1]), '.3eh')
    '<1.414e+00, 7.854e-01>'
    >>> format(Vector([1, 1]), '0.5fh')
    '<1.41421, 0.78540>'
    >>> format(Vector([1, 1, 1]), 'h')  # doctest:+ELLIPSIS
    '<1.73205..., 0.95531..., 0.78539...>'
    >>> format(Vector([2, 2, 2]), '.3eh')
    '<3.464e+00, 9.553e-01, 7.854e-01>'
    >>> format(Vector([0, 0, 0]), '0.5fh')
    '<0.00000, 0.00000, 0.00000>'
    >>> format(Vector([-1, -1, -1, -1]), 'h')  # doctest:+ELLIPSIS
    '<2.0, 2.09439..., 2.18627..., 3.92699...>'
    >>> format(Vector([2, 2, 2, 2]), '.3eh')
    '<4.000e+00, 1.047e+00, 9.553e-01, 7.854e-01>'
    >>> format(Vector([0, 1, 0, 0]), '0.5fh')
    '<1.00000, 1.57080, 0.00000, 0.00000>'
"""

from array import array
import reprlib
import math
import functools
import operator
import itertools  


class Vector:
    typecode = 'd'

    def __init__(self, components):
        self._components = array(self.typecode, components)

    def __iter__(self):
        return iter(self._components)

    def __repr__(self):
        components = reprlib.repr(self._components)
        components = components[components.find('['):-1]
        return 'Vector({})'.format(components)

    def __str__(self):
        return str(tuple(self))

    def __bytes__(self):
        return (bytes([ord(self.typecode)]) +
                bytes(self._components))

    def __eq__(self, other):
        return (len(self) == len(other) and
                all(a == b for a, b in zip(self, other)))

    def __hash__(self):
        hashes = (hash(x) for x in self)
        return functools.reduce(operator.xor, hashes, 0)

    def __abs__(self):
        return math.sqrt(sum(x * x for x in self))

    def __bool__(self):
        return bool(abs(self))

    def __len__(self):
        return len(self._components)

    def __getitem__(self, key):
        if isinstance(key, slice):
            cls = type(self)
            return cls(self._components[key])
        index = operator.index(key)
        return self._components[index]

    shortcut_names = 'xyzt'

    def __getattr__(self, name):
        cls = type(self)
        if len(name) == 1:
            pos = cls.shortcut_names.find(name)
            if 0 <= pos < len(self._components):
                return self._components[pos]
        msg = '{.__name__!r} object has no attribute {!r}'
        raise AttributeError(msg.format(cls, name))

    def angle(self, n):  
        r = math.sqrt(sum(x * x for x in self[n:]))
        a = math.atan2(r, self[n-1])
        if (n == len(self) - 1) and (self[-1] < 0):
            return math.pi * 2 - a
        else:
            return a

    def angles(self):  
        return (self.angle(n) for n in range(1, len(self)))

    def __format__(self, fmt_spec=''):
        if fmt_spec.endswith('h'):  # hyperspherical coordinates
            fmt_spec = fmt_spec[:-1]
            coords = itertools.chain([abs(self)],
                                     self.angles())  
            outer_fmt = '<{}>'  
        else:
            coords = self
            outer_fmt = '({})'  
        components = (format(c, fmt_spec) for c in coords)  
        return outer_fmt.format(', '.join(components))  

    @classmethod
    def frombytes(cls, octets):
        typecode = chr(octets[0])
        memv = memoryview(octets[1:]).cast(typecode)
        return cls(memv)

Import itertools to use chain function in __format__.

Compute one of the angular coordinates, using formulas adapted from the n-sphere article.

Create generator expression to compute all angular coordinates on demand.

Use itertools.chain to produce genexp to iterate seamlessly over the magnitude and the angular coordinates.

Configure spherical coordinate display with angular brackets.

Configure Cartesian coordinate display with parentheses.

Create generator expression to format each coordinate item on demand.

Plug formatted components separated by commas inside brackets or parentheses.

This concludes our mission for this chapter. The Vector class will be enhanced with infix operators in [Link to Come], but our goal here was to explore techniques for coding special methods that are useful in a wide variety of collection classes.

Chapter Summary

The Vector example in this chapter was designed to be compatible with Vector2d, except for the use of a different constructor signature accepting a single iterable argument, just like the built-in sequence types do. The fact that Vector behaves as a sequence just by implementing __getitem__ and __len__ prompted a discussion of protocols, the informal interfaces used in duck-typed languages.

We then looked at how the my_seq[a:b:c] syntax works behind the scenes, by creating a slice(a, b, c) object and handing it to __getitem__. Armed with this knowledge, we made Vector respond correctly to slicing, by returning new Vector instances, just like a Pythonic sequence is expected to do.

The next step was to provide read-only access to the first few Vector components using notation such as my_vec.x. We did it by implementing __getattr__. Doing that opened the possibility of tempting the user to assign to those special components by writing my_vec.x = 7, revealing a potential bug. We fixed it by implementing __setattr__ as well, to forbid assigning values to single-letter attributes. Very often, when you code a __getattr__ you need to add __setattr__ too, in order to avoid inconsistent behavior.

Implementing the __hash__ function provided the perfect context for using functools.reduce, because we needed to apply the xor operator ^ in succession to the hashes of all Vector components to produce an aggregate hash code for the whole Vector. After applying reduce in __hash__, we used the all reducing built-in to create a more efficient __eq__ method.

The last enhancement to Vector was to reimplement the __format__ method from Vector2d by supporting spherical coordinates as an alternative to the default Cartesian coordinates. We used quite a bit of math and several generators to code __format__ and its auxiliary functions, but these are implementation details—and we’ll come back to the generators in [Link to Come]. The goal of that last section was to support a custom format, thus fulfilling the promise of a Vector that could do everything a Vector2d did, and more.

As we did in Chapter 11, here we often looked at how standard Python objects behave, to emulate them and provide a “Pythonic” look-and-feel to Vector.

In [Link to Come], we will implement several infix operators on Vector. The math will be much simpler than that in the angle() method here, but exploring how infix operators work in Python is a great lesson in OO design. But before we get to operator overloading, we’ll step back from working on one class and look at organizing multiple classes with interfaces and inheritance, the subjects of Chapters [Link to Come] and [Link to Come].

Further Reading

Most special methods covered in the Vector example also appear in the Vector2d example from Chapter 11, so the references in “Further Reading” are all relevant here.

The powerful reduce higher-order function is also known as fold, accumulate, aggregate, compress, and inject. For more information, see Wikipedia’s “Fold (higher-order function)” article, which presents applications of that higher-order function with emphasis on functional programming with recursive data structures. The article also includes a table listing fold-like functions in dozens of programming languages.

What’s New in Python 2.5 has a short explanation of __index__, designed to support __getitem__ methods, as we saw in “A Slice-Aware __getitem__”. PEP 357—Allowing Any Object to be Used for Slicing details the need for it from the perspective of an implementor of a C-extension—Travis Oliphant, the primary creator of NumPy. Oliphant’s many contributions to Python made it a leading scientific computing language, which then positioned it to lead the way in machine learning applications.